{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{hint}\n",
    "✨✨✨ **Run this notebook on Google Colab** ✨✨✨\n",
    "\n",
    "You can [run this notebook yourself with Google Colab](https://colab.research.google.com/github/Eventual-Inc/Daft/blob/main/tutorials/window_functions/window_functions.ipynb)!\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Window Functions with Daft: The Great Chocolate Race\n",
    "\n",
    "Window functions allow you to perform calculations across a set of rows related to the current row. These calculations happen within a defined \"window\" of data, giving developers and analysts the ability to create running totals, rankings, and comparisons without altering the dataset.\n",
    "\n",
    "In this tutorial, we'll explore how Daft's window functions can transform complex analytical challenges into elegant solutions. We'll follow an entertaining twist on a classic scenario -- a chocholate eating contest between a tortoise and a hare -- where we'll analyze their performance using different window function techniques. \n",
    "\n",
    "Without window functions, these operations would require multiple self-joins, temporary tables, and complex aggregations. We'll demonstrate how window functions can reduce dozens of lines of complex code to just a few simple, readable lines.\n",
    "\n",
    "**What You'll Learn**\n",
    "\n",
    "* How window functions dramatically simplify complex analytics queries\n",
    "* Four key window specifications in Daft: \n",
    "    * Partition by\n",
    "    * Partition by + order by\n",
    "    * Partition by + order by + rows between\n",
    "    * Partition by + order by + range between\n",
    "* Practical applications including:\n",
    "    * Creating rankings\n",
    "    * Calculating deltas between events\n",
    "    * Analyzing moving windows of activity\n",
    "    * Computing cumulative totals\n",
    "\n",
    "See also [Window Functions API Docs](https://docs.daft.ai/en/stable/api/window/) in Daft Documentation.\n",
    "\n",
    "![image1.jpeg](image1.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup and Imports\n",
    "\n",
    "First, let's install the necessary python packages and import the libraries we'll use through this tutorial:\n",
    "* [**Daft**](www.daft.ai): Showcasing powerful window functions native in Daft, a unified data engine that leverage both a familiar Python DataFrame API and SQL\n",
    "* [**Pandas**](https://pola.rs/): While Daft handles our main data processing, we'll use Pandas to visualize results\n",
    "* [**Matplotlib**](https://matplotlib.org/): For creating visualizations that help us understand the patterns in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install daft pandas matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from collections import defaultdict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import numpy as np\n",
    "\n",
    "import daft\n",
    "from daft import Window, col\n",
    "from daft.functions import rank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Generation: Setting Up Our Chocolate Contest\n",
    "\n",
    "To make our example fun and illustrative, we'll simulate a chocolate eating contest between a tortoise and a hare. In this contest:\n",
    "* The tortoise is slow but steady, eating chocolates consistently over time\n",
    "* The hare is fast but takes a break in the middle (between time 700 and 900)\n",
    "* Both contests eat exactly 1,111 chocolates by the end\n",
    "\n",
    "Here's how we'll generate this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(42)\n",
    "\n",
    "min_time = 0\n",
    "max_hare_time = 1000\n",
    "max_tortoise_time = 963\n",
    "num_hare_events = 100\n",
    "num_tortoise_events = 300\n",
    "\n",
    "hare_times = sorted(random.sample(range(min_time, max_hare_time + 1), num_hare_events))\n",
    "hare_times = [t for t in hare_times if t < 700 or t > 900]\n",
    "\n",
    "tortoise_times = sorted(random.sample(range(min_time, max_tortoise_time + 1), num_tortoise_events))\n",
    "\n",
    "hare_chocolates = [random.randint(1, 20) for _ in range(len(hare_times))]\n",
    "tortoise_chocolates = [random.randint(1, 20) for _ in range(len(tortoise_times))]\n",
    "\n",
    "target_sum = 1111\n",
    "\n",
    "current_sum = sum(hare_chocolates)\n",
    "while current_sum != target_sum:\n",
    "    idx = random.randint(0, len(hare_chocolates) - 1)\n",
    "    if current_sum < target_sum:\n",
    "        hare_chocolates[idx] += 1\n",
    "        current_sum += 1\n",
    "    else:\n",
    "        if hare_chocolates[idx] > 1:\n",
    "            hare_chocolates[idx] -= 1\n",
    "            current_sum -= 1\n",
    "\n",
    "current_sum = sum(tortoise_chocolates)\n",
    "while current_sum != target_sum:\n",
    "    idx = random.randint(0, len(tortoise_chocolates) - 1)\n",
    "    if current_sum < target_sum:\n",
    "        tortoise_chocolates[idx] += 1\n",
    "        current_sum += 1\n",
    "    else:\n",
    "        if tortoise_chocolates[idx] > 1:\n",
    "            tortoise_chocolates[idx] -= 1\n",
    "            current_sum -= 1\n",
    "\n",
    "data = []\n",
    "for i, t in enumerate(hare_times):\n",
    "    data.append({\"contestant\": \"hare\", \"time\": t, \"chocolates\": hare_chocolates[i]})\n",
    "for i, t in enumerate(tortoise_times):\n",
    "    data.append({\"contestant\": \"tortoise\", \"time\": t, \"chocolates\": tortoise_chocolates[i]})\n",
    "\n",
    "random.shuffle(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Our Dataset\n",
    "\n",
    "Let's first create a Daft Dataframe from our generated data and take a look at what we're working with. This gives us a dataset with 3 columns:\n",
    "* `contestant`: either \"hare\" or \"tortoise\"\n",
    "* `time`: when they ate chocolates (time units from 0 to ~1000)\n",
    "* `chocolates`: how many chocolates they ate at that particular time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chocolates</th>\n",
       "      <th>contestant</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>hare</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>tortoise</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>hare</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>tortoise</td>\n",
       "      <td>811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>tortoise</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>1</td>\n",
       "      <td>tortoise</td>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>13</td>\n",
       "      <td>tortoise</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>7</td>\n",
       "      <td>hare</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>8</td>\n",
       "      <td>tortoise</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>7</td>\n",
       "      <td>tortoise</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>379 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     chocolates contestant  time\n",
       "0            10       hare   284\n",
       "1             7   tortoise   382\n",
       "2             9       hare   464\n",
       "3             1   tortoise   811\n",
       "4             1   tortoise   953\n",
       "..          ...        ...   ...\n",
       "374           1   tortoise   931\n",
       "375          13   tortoise   252\n",
       "376           7       hare   955\n",
       "377           8   tortoise   535\n",
       "378           7   tortoise   698\n",
       "\n",
       "[379 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = daft.from_pylist(data)\n",
    "df.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Optional) To better understand the early stages of our race, let's visualize how many chocolates each contestant ate in the first 100 time units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hare_dict = defaultdict(int)\n",
    "tortoise_dict = defaultdict(int)\n",
    "\n",
    "for t, c in zip(hare_times, hare_chocolates):\n",
    "    if t <= 100:\n",
    "        hare_dict[t] += c\n",
    "\n",
    "for t, c in zip(tortoise_times, tortoise_chocolates):\n",
    "    if t <= 100:\n",
    "        tortoise_dict[t] += c\n",
    "\n",
    "shown_times = sorted(set(hare_dict.keys()) | set(tortoise_dict.keys()))\n",
    "\n",
    "bar_width = 0.45\n",
    "x = np.arange(len(shown_times))\n",
    "\n",
    "hare_vals = [hare_dict[t] for t in shown_times]\n",
    "tortoise_vals = [tortoise_dict[t] for t in shown_times]\n",
    "\n",
    "hare_color = \"#f28c38\"\n",
    "tortoise_color = \"#6c9b41\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "ax.bar(x - bar_width / 2, hare_vals, width=bar_width, label=\"Hare\", color=hare_color)\n",
    "ax.bar(x + bar_width / 2, tortoise_vals, width=bar_width, label=\"Tortoise\", color=tortoise_color)\n",
    "\n",
    "ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n",
    "\n",
    "xtick_labels = [str(t) for t in shown_times]\n",
    "plt.xticks(x, xtick_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "plt.text(x[-1] + 1.5, -1.5, \"⋯\", fontsize=20, ha=\"center\", va=\"top\", transform=ax.transData)\n",
    "\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Chocolates Eaten\")\n",
    "ax.set_title(\"Chocolates Eaten Per Time Unit (up to t = 100)\")\n",
    "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Already we can see the different eating patterns starting to emerge! The hare tends to eat more chocolates in each sitting, while the tortoise has more frequent but smaller sessions.\n",
    "\n",
    "Now let's start demonstrating the power of window functions by solving increasingly complex analytical queries."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge 1: Ranking Chcolate Sessions by Size\n",
    "\n",
    "Our first challenge is to rank each contestant's chocolate-eating sessions from largest to smallest. This is a perfect use case for window functions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without Window Functions\n",
    "\n",
    "*Without* window functions, we'd need to perform a self-join and complex aggregation. This approach requires:\n",
    "1. A self-join to compare each session with all other sessions\n",
    "2. Filtering to count sessions with more chocolates\n",
    "3. Grouping and aggregating to calculate the rank\n",
    "4. Joining back to the original data\n",
    "5. Handling nulls for top-ranked sessions\n",
    "\n",
    "This approach is complex, error-prone, and hard to understand at a glance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table class=\"dataframe\">\n",
       "<thead><tr><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">contestant<br />Utf8</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">chocolates<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">session_rank<br />Int32</th></tr></thead>\n",
       "<tbody>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">25</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">24</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">9</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">23</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">13</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">17</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">28</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">16</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">29</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">22</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">33</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">21</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">37</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">19</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">39</div></td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<small>(Showing first 8 of 40 rows)</small>\n",
       "</div>"
      ],
      "text/plain": [
       "╭────────────┬────────────┬──────────────╮\n",
       "│ contestant ┆ chocolates ┆ session_rank │\n",
       "│ ---        ┆ ---        ┆ ---          │\n",
       "│ Utf8       ┆ Int64      ┆ Int32        │\n",
       "╞════════════╪════════════╪══════════════╡\n",
       "│ hare       ┆ 25         ┆ 1            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 24         ┆ 9            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 23         ┆ 13           │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 17         ┆ 28           │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 16         ┆ 29           │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 22         ┆ 33           │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 21         ┆ 37           │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 19         ┆ 39           │\n",
       "╰────────────┴────────────┴──────────────╯\n",
       "\n",
       "(Showing first 8 of 40 rows)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_df = (\n",
    "    df.join(df, left_on=[\"contestant\"], right_on=[\"contestant\"], how=\"inner\", suffix=\"_r\")\n",
    "    .where(col(\"chocolates_r\") > col(\"chocolates\"))\n",
    "    .groupby([\"contestant\", \"chocolates\"])\n",
    "    .agg((col(\"chocolates_r\").count() + 1).alias(\"session_rank\"))\n",
    ")\n",
    "\n",
    "df.join(agg_df, on=[\"contestant\", \"chocolates\"], how=\"left\").with_column(\n",
    "    \"session_rank\", col(\"session_rank\").fill_null(1).cast(\"int\")\n",
    ").select(\"contestant\", \"chocolates\", \"session_rank\").distinct().sort([\"contestant\", \"session_rank\"]).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With Window Functions\n",
    "\n",
    "*With* window functions, the same analysis is simplified:\n",
    "1. Define a window partitioned by contestant and ordered by chocolates (descending)\n",
    "2. Apply the [`dense_rank()`](https://docs.daft.ai/en/stable/api/window/#daft.functions.dense_rank) function over this window\n",
    "\n",
    "The window functions handles all the comparison logic internally, making our code more concise and more efficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table class=\"dataframe\">\n",
       "<thead><tr><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">contestant<br />Utf8</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">chocolates<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">session_rank<br />UInt64</th></tr></thead>\n",
       "<tbody>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">25</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">24</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">2</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">23</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">3</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">22</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">4</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">21</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">5</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">20</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">6</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">19</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">7</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">18</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">8</div></td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<small>(Showing first 8 of 40 rows)</small>\n",
       "</div>"
      ],
      "text/plain": [
       "╭────────────┬────────────┬──────────────╮\n",
       "│ contestant ┆ chocolates ┆ session_rank │\n",
       "│ ---        ┆ ---        ┆ ---          │\n",
       "│ Utf8       ┆ Int64      ┆ UInt64       │\n",
       "╞════════════╪════════════╪══════════════╡\n",
       "│ hare       ┆ 25         ┆ 1            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 24         ┆ 2            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 23         ┆ 3            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 22         ┆ 4            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 21         ┆ 5            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 20         ┆ 6            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 19         ┆ 7            │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 18         ┆ 8            │\n",
       "╰────────────┴────────────┴──────────────╯\n",
       "\n",
       "(Showing first 8 of 40 rows)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_contestant_chocolates = Window().partition_by(\"contestant\").order_by(\"chocolates\", desc=True)\n",
    "\n",
    "from daft.functions import dense_rank\n",
    "\n",
    "df.with_column(\"session_rank\", dense_rank().over(by_contestant_chocolates)).select(\n",
    "    \"contestant\", \"chocolates\", \"session_rank\"\n",
    ").distinct().sort([\"contestant\", \"session_rank\"]).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Optional) Let's visualize these rankings to better understand each contestant's eating patterns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranked_df_pd = (\n",
    "    df.with_column(\"session_rank\", rank().over(by_contestant_chocolates))\n",
    "    .select(\"contestant\", \"chocolates\", \"session_rank\")\n",
    "    .distinct()\n",
    "    .sort([\"contestant\", \"session_rank\"])\n",
    "    .collect()\n",
    "    .to_pandas()\n",
    ")\n",
    "\n",
    "colors = {\"hare\": \"#f28c38\", \"tortoise\": \"#6c9b41\"}\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(12, 10), sharey=True)\n",
    "\n",
    "for ax, contestant in zip(axes, [\"hare\", \"tortoise\"]):\n",
    "    subset = ranked_df_pd[ranked_df_pd[\"contestant\"] == contestant]\n",
    "    subset = subset.sort_values(\"chocolates\", ascending=False)\n",
    "\n",
    "    ax.plot(\n",
    "        subset[\"chocolates\"],\n",
    "        subset[\"session_rank\"],\n",
    "        color=colors[contestant],\n",
    "        marker=\"o\",\n",
    "        linestyle=\"-\",\n",
    "    )\n",
    "\n",
    "    ax.set_title(f\"{contestant.capitalize()} - Chocolate Rank vs Chocolates\")\n",
    "    ax.set_ylabel(\"Session Rank\")\n",
    "    ax.invert_yaxis()\n",
    "    ax.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "axes[1].set_xlabel(\"Number of Chocolates\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This visualization confirms our hunch: the hare has fewer but more intense chocolate-eating session (steeper curve), while the tortoise has many more sessions with a more gradual distribution of chocolate quantities."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge 2: Chocolate Consumption Change Between Sessions\n",
    "\n",
    "Next, let's understand how chocolate consumption changes from one session to the next for each contestant.\n",
    "\n",
    "*Without* window functions, we need another complex set of joins. This requires:\n",
    "1. A self-join to find all earlier sessions\n",
    "2. Finding the most recent previous session\n",
    "3. Another join to get the chocolate count from that session\n",
    "4. Compute the difference\n",
    "\n",
    "*With* window functions, we use the [`lag()`](https://docs.daft.ai/en/stable/api/window/#daft.expressions.Expression.lag) function to access the previous row in our ordered window. Here, `lag(1)` retrieves the value from *one row before* the current row in the window. This reduces our complex multi-join solution to just two lines of code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table class=\"dataframe\">\n",
       "<thead><tr><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">chocolates<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">contestant<br />Utf8</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">time<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">delta_from_previous<br />Int64</th></tr></thead>\n",
       "<tbody>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">14</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">6</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">None</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">14</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">25</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">0</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">9</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">27</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">-5</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">14</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">30</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">5</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">10</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">32</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">-4</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">12</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">44</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">2</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">18</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">46</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">6</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">7</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">71</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">-11</div></td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<small>(Showing first 8 of 379 rows)</small>\n",
       "</div>"
      ],
      "text/plain": [
       "╭────────────┬────────────┬───────┬─────────────────────╮\n",
       "│ chocolates ┆ contestant ┆ time  ┆ delta_from_previous │\n",
       "│ ---        ┆ ---        ┆ ---   ┆ ---                 │\n",
       "│ Int64      ┆ Utf8       ┆ Int64 ┆ Int64               │\n",
       "╞════════════╪════════════╪═══════╪═════════════════════╡\n",
       "│ 14         ┆ hare       ┆ 6     ┆ None                │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 14         ┆ hare       ┆ 25    ┆ 0                   │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 9          ┆ hare       ┆ 27    ┆ -5                  │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 14         ┆ hare       ┆ 30    ┆ 5                   │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 10         ┆ hare       ┆ 32    ┆ -4                  │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 12         ┆ hare       ┆ 44    ┆ 2                   │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 18         ┆ hare       ┆ 46    ┆ 6                   │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 7          ┆ hare       ┆ 71    ┆ -11                 │\n",
       "╰────────────┴────────────┴───────┴─────────────────────╯\n",
       "\n",
       "(Showing first 8 of 379 rows)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_contestant_time = Window().partition_by(\"contestant\").order_by(\"time\", desc=False)\n",
    "\n",
    "df.with_column(\n",
    "    \"delta_from_previous\", (col(\"chocolates\") - col(\"chocolates\").lag(1, default=None).over(by_contestant_time))\n",
    ").sort([\"contestant\", \"time\"]).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Optional) Let's visualize these changes to better understand the pattern:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta_df_pd = (\n",
    "    df.with_column(\n",
    "        \"delta_from_previous\",\n",
    "        (\n",
    "            col(\"chocolates\")\n",
    "            - col(\"chocolates\").lag(1, default=None).over(Window().partition_by(\"contestant\").order_by(\"time\"))\n",
    "        ),\n",
    "    )\n",
    "    .select(\"contestant\", \"time\", \"delta_from_previous\")\n",
    "    .collect()\n",
    "    .to_pandas()\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "colors = {\"hare\": \"#f28c38\", \"tortoise\": \"#6c9b41\"}\n",
    "\n",
    "for contestant in delta_df_pd[\"contestant\"].unique():\n",
    "    subset = delta_df_pd[delta_df_pd[\"contestant\"] == contestant]\n",
    "    ax.plot(\n",
    "        subset[\"time\"],\n",
    "        subset[\"delta_from_previous\"],\n",
    "        label=contestant.capitalize(),\n",
    "        color=colors[contestant],\n",
    "        drawstyle=\"steps-post\",\n",
    "    )\n",
    "\n",
    "ax.axhline(0, color=\"gray\", linestyle=\"--\", linewidth=1)\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Change in Chocolates From Previous\")\n",
    "ax.set_title(\"Chocolate Consumption Change Over Time\")\n",
    "ax.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the hare's erratic consumption pattern with high variability, compared to the tortoise's more steady appracoh. Also note the gap in the hare's data, reflecting the nap it took between times 700 and 900."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge 3: Peak Chocolate-Eating Sprees\n",
    "\n",
    "Now, let's identify when each contestant had their most impressive chocolate-eating sprees. We'll define a *spree* as the total chocoaltes eaten within a 100-time-unit window (50 units before and 50 units after a given point).\n",
    "\n",
    "_Without_ window functions, this gets even more complex. For this, we need:\n",
    "1. A self-join with conditions to find sessions within our time window\n",
    "2. Aggregation to sum chocolates in that window\n",
    "3. Separate filtering and sorting for each contestant\n",
    "4. Concatenation to combine results\n",
    "\n",
    "*With* window functions, we introduce a powerful concept: the frame specification with [`rows_between()`](https://docs.daft.ai/en/stable/api/window/#daft.window.Window.rows_between). This lets us define precisely which rows to include in our window.\n",
    "\n",
    "The `.rows_between(-50,50)` specification tells Daft to include 50 rows before and 50 rows after the current row in the calculation. We need:\n",
    "1. Calculate the sum of chocolates within this window\n",
    "2. Rank these sums to find the top sprees\n",
    "3. Filter to keep only the top 3 for each contestant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table class=\"dataframe\">\n",
       "<thead><tr><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">contestant<br />Utf8</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">time<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">chocolates<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">chocolates_within_100min<br />Int64</th></tr></thead>\n",
       "<tbody>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">300</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">18</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">284</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">10</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">281</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">20</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">250</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">15</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">228</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">10</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">233</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">20</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">238</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">23</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">270</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">24</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">1111</div></td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<small>(Showing first 8 of 28 rows)</small>\n",
       "</div>"
      ],
      "text/plain": [
       "╭────────────┬───────┬────────────┬──────────────────────────╮\n",
       "│ contestant ┆ time  ┆ chocolates ┆ chocolates_within_100min │\n",
       "│ ---        ┆ ---   ┆ ---        ┆ ---                      │\n",
       "│ Utf8       ┆ Int64 ┆ Int64      ┆ Int64                    │\n",
       "╞════════════╪═══════╪════════════╪══════════════════════════╡\n",
       "│ hare       ┆ 300   ┆ 18         ┆ 1111                     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 284   ┆ 10         ┆ 1111                     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 281   ┆ 20         ┆ 1111                     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 250   ┆ 15         ┆ 1111                     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 228   ┆ 10         ┆ 1111                     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 233   ┆ 20         ┆ 1111                     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 238   ┆ 23         ┆ 1111                     │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ hare       ┆ 270   ┆ 24         ┆ 1111                     │\n",
       "╰────────────┴───────┴────────────┴──────────────────────────╯\n",
       "\n",
       "(Showing first 8 of 28 rows)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_contestant_time_range = Window().partition_by(\"contestant\").order_by(\"time\", desc=False).rows_between(-50, 50)\n",
    "by_chocolate_spree = Window().partition_by(\"contestant\").order_by(\"chocolates_within_100min\", desc=True)\n",
    "\n",
    "df.with_column(\"chocolates_within_100min\", col(\"chocolates\").sum().over(by_contestant_time_range)).with_column(\n",
    "    \"time_window_chocolates_rank\", rank().over(by_chocolate_spree)\n",
    ").filter(col(\"time_window_chocolates_rank\") <= 3).select(\n",
    "    \"contestant\", \"time\", \"chocolates\", \"chocolates_within_100min\"\n",
    ").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Optional) Let's visualize these sprees over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spree_df_pd = (\n",
    "    df.with_column(\n",
    "        \"chocolates_within_100min\",\n",
    "        col(\"chocolates\").sum().over(Window().partition_by(\"contestant\").order_by(\"time\").range_between(-50, 50)),\n",
    "    )\n",
    "    .select(\"contestant\", \"time\", \"chocolates_within_100min\")\n",
    "    .collect()\n",
    "    .to_pandas()\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "colors = {\"hare\": \"#f28c38\", \"tortoise\": \"#6c9b41\"}\n",
    "\n",
    "for contestant in spree_df_pd[\"contestant\"].unique():\n",
    "    subset = spree_df_pd[spree_df_pd[\"contestant\"] == contestant]\n",
    "    ax.plot(\n",
    "        subset[\"time\"],\n",
    "        subset[\"chocolates_within_100min\"],\n",
    "        label=contestant.capitalize(),\n",
    "        color=colors[contestant],\n",
    "        drawstyle=\"steps-post\",\n",
    "    )\n",
    "\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Chocolates in 100-Min Window\")\n",
    "ax.set_title(\"Peak Chocolate-Eating Sprees\")\n",
    "ax.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph reveals the intensity of eating over time. Notice how for the hare, the peak sprees happen at the beginning and ending of the race, with that characteristic gap in the middle. The tortoise maintains more consistent sprees throughout."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge 4: Calculating Cumulative Chocolates\n",
    "\n",
    "Finally, let's track the total chocolates eaten by each contestant over time.\n",
    "\n",
    "*Without* window functions, we need:\n",
    "1. A self-join to find all earlier and current sessions\n",
    "2. Aggregation to sum the chocolates\n",
    "3. A join back to the original data\n",
    "\n",
    "*With* window functions, this becomes simplified. When no frame is specified, Daft defaults to including all previous rows and the current row in the window. This is perfect for calculating running totals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table class=\"dataframe\">\n",
       "<thead><tr><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">chocolates<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">contestant<br />Utf8</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">time<br />Int64</th><th style=\"text-wrap: nowrap; max-width:192px; overflow:auto; text-align:left\">cumulative_chocolates<br />Int64</th></tr></thead>\n",
       "<tbody>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">14</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">6</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">37</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">14</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">25</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">51</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">9</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">27</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">61</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">14</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">30</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">59</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">10</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">32</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">63</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">12</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">44</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">61</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">18</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">46</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">60</div></td></tr>\n",
       "<tr><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">7</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">hare</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">71</div></td><td><div style=\"text-align:left; max-width:192px; max-height:64px; overflow:auto\">69</div></td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "<small>(Showing first 8 of 379 rows)</small>\n",
       "</div>"
      ],
      "text/plain": [
       "╭────────────┬────────────┬───────┬───────────────────────╮\n",
       "│ chocolates ┆ contestant ┆ time  ┆ cumulative_chocolates │\n",
       "│ ---        ┆ ---        ┆ ---   ┆ ---                   │\n",
       "│ Int64      ┆ Utf8       ┆ Int64 ┆ Int64                 │\n",
       "╞════════════╪════════════╪═══════╪═══════════════════════╡\n",
       "│ 14         ┆ hare       ┆ 6     ┆ 37                    │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 14         ┆ hare       ┆ 25    ┆ 51                    │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 9          ┆ hare       ┆ 27    ┆ 61                    │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 14         ┆ hare       ┆ 30    ┆ 59                    │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 10         ┆ hare       ┆ 32    ┆ 63                    │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 12         ┆ hare       ┆ 44    ┆ 61                    │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 18         ┆ hare       ┆ 46    ┆ 60                    │\n",
       "├╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n",
       "│ 7          ┆ hare       ┆ 71    ┆ 69                    │\n",
       "╰────────────┴────────────┴───────┴───────────────────────╯\n",
       "\n",
       "(Showing first 8 of 379 rows)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_contestant_time = Window().partition_by(\"contestant\").order_by(\"time\", desc=False).rows_between(-2, 2)\n",
    "\n",
    "df.with_column(\n",
    "    \"cumulative_chocolates\",\n",
    "    col(\"chocolates\").sum().over(by_contestant_time),\n",
    ").sort([\"contestant\", \"time\"]).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Optional) Let's visualize these cumulative totals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true,
    "jupyter": {
     "source_hidden": true
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_df_pd = (\n",
    "    df.with_column(\n",
    "        \"cumulative_chocolates\", col(\"chocolates\").sum().over(Window().partition_by(\"contestant\").order_by(\"time\"))\n",
    "    )\n",
    "    .select(\"contestant\", \"time\", \"cumulative_chocolates\")\n",
    "    .collect()\n",
    "    .to_pandas()\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "colors = {\"hare\": \"#f28c38\", \"tortoise\": \"#6c9b41\"}\n",
    "\n",
    "for contestant in cumulative_df_pd[\"contestant\"].unique():\n",
    "    subset = cumulative_df_pd[cumulative_df_pd[\"contestant\"] == contestant]\n",
    "    ax.plot(\n",
    "        subset[\"time\"],\n",
    "        subset[\"cumulative_chocolates\"],\n",
    "        label=contestant.capitalize(),\n",
    "        color=colors[contestant],\n",
    "        drawstyle=\"steps-post\",\n",
    "    )\n",
    "\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_ylabel(\"Cumulative Chocolates\")\n",
    "ax.set_title(\"Cumulative Chocolates Over Time\")\n",
    "ax.grid(True, linestyle=\"--\", alpha=0.7)\n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there's our classic tortoise vs hare story in data form! We can clearly see:\n",
    "* The hare's quick start, building an early lead\n",
    "* The plateau during the nap (between times 700 and 900)\n",
    "* The tortoise's steady progress throughout\n",
    "* The hare's final sprint after waking up\n",
    "* Both contestants finish with exactly the same total (1,111 chocolates)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bringing It All Together\n",
    "\n",
    "Now that we've explored each window function technique individually, let's combine them into a single comprehensive analysis.\n",
    "\n",
    "![image2.jpeg](image2.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_contestant_time = Window().partition_by(\"contestant\").order_by(\"time\", desc=False)\n",
    "by_contestant_time_range = by_contestant_time.range_between(-50, 50)\n",
    "by_chocolates_desc = Window().partition_by(\"contestant\").order_by(\"chocolates\", desc=True)\n",
    "by_spree_desc = Window().partition_by(\"contestant\").order_by(\"chocolates_within_100min\", desc=True)\n",
    "\n",
    "api_df = (\n",
    "    df.select(\n",
    "        col(\"contestant\"),\n",
    "        col(\"time\"),\n",
    "        col(\"chocolates\"),\n",
    "        col(\"chocolates\").sum().over(by_contestant_time).alias(\"cumulative_chocolates\"),\n",
    "        col(\"chocolates\").sum().over(by_contestant_time_range).alias(\"chocolates_within_100min\"),\n",
    "        rank().over(by_chocolates_desc).alias(\"session_rank\"),\n",
    "        (col(\"chocolates\") - col(\"chocolates\").lag(1, default=0).over(by_contestant_time)).alias(\"delta_from_previous\"),\n",
    "    )\n",
    "    .with_column(\"time_window_chocolates_rank\", rank().over(by_spree_desc))\n",
    "    .sort([\"contestant\", \"time\"])\n",
    ").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With just a handful of window function calls, we've calculated:\n",
    "* Cumulative chocolates eaten over time\n",
    "* Chocolates eaten within 100-minute sprees\n",
    "* Session rankings by chocolate count\n",
    "* Changes in consumption between consecutive sessions\n",
    "* Rankings of the most intense eating spress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SQL\n",
    "\n",
    "For those more familiar with SQL, Daft also supports these window operations through SQL syntax. See also [Window Functions SQL Reference](https://docs.daft.ai/en/stable/sql/window_functions/) in Daft Documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_df = daft.sql(\n",
    "    \"\"\"\n",
    "    WITH base AS (\n",
    "        SELECT\n",
    "            contestant,\n",
    "            time,\n",
    "            chocolates,\n",
    "            SUM(chocolates) OVER (\n",
    "                PARTITION BY contestant\n",
    "                ORDER BY time\n",
    "            ) AS cumulative_chocolates,\n",
    "            SUM(chocolates) OVER (\n",
    "                PARTITION BY contestant\n",
    "                ORDER BY time\n",
    "                RANGE BETWEEN 50 PRECEDING AND 50 FOLLOWING\n",
    "            ) AS chocolates_within_100min,\n",
    "            RANK() OVER (\n",
    "                PARTITION BY contestant\n",
    "                ORDER BY chocolates DESC\n",
    "            ) AS session_rank,\n",
    "            chocolates - LAG(chocolates, 1, 0) OVER (\n",
    "                PARTITION BY contestant\n",
    "                ORDER BY time\n",
    "            ) AS delta_from_previous\n",
    "        FROM df\n",
    "    )\n",
    "    SELECT *,\n",
    "        RANK() OVER (\n",
    "            PARTITION BY contestant\n",
    "            ORDER BY chocolates_within_100min DESC\n",
    "        ) AS time_window_chocolates_rank\n",
    "    FROM base\n",
    "    ORDER BY contestant, time\n",
    "    \"\"\"\n",
    ").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Alternative: ...Without Window Functions\n",
    "\n",
    "To fully appreciate window functions, let's look at what it would take to produce the same comprehensive analysis without them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumulative_df = (\n",
    "    df.join(df, left_on=\"contestant\", right_on=\"contestant\", suffix=\"_r\", how=\"inner\")\n",
    "    .where(col(\"time_r\") <= col(\"time\"))\n",
    "    .groupby([\"contestant\", \"time\"])\n",
    "    .agg(col(\"chocolates_r\").sum().alias(\"cumulative_chocolates\"))\n",
    ")\n",
    "\n",
    "window_df = (\n",
    "    df.join(df, left_on=\"contestant\", right_on=\"contestant\", suffix=\"_r\", how=\"inner\")\n",
    "    .where((col(\"time_r\") >= col(\"time\") - 50) & (col(\"time_r\") <= col(\"time\") + 50))\n",
    "    .groupby([\"contestant\", \"time\"])\n",
    "    .agg(col(\"chocolates_r\").sum().alias(\"chocolates_within_100min\"))\n",
    ")\n",
    "\n",
    "rank_df = (\n",
    "    df.join(df, left_on=\"contestant\", right_on=\"contestant\", suffix=\"_r\", how=\"inner\")\n",
    "    .where(col(\"chocolates_r\") > col(\"chocolates\"))\n",
    "    .groupby([\"contestant\", \"time\", \"chocolates\"])\n",
    "    .agg((col(\"chocolates_r\").count() + 1).alias(\"session_rank\"))\n",
    ")\n",
    "\n",
    "previous_df = (\n",
    "    df.join(df, left_on=\"contestant\", right_on=\"contestant\", suffix=\"_r\", how=\"inner\")\n",
    "    .where(col(\"time_r\") < col(\"time\"))\n",
    "    .groupby([\"contestant\", \"time\"])\n",
    "    .agg(col(\"time_r\").max().alias(\"previous_time\"))\n",
    ")\n",
    "\n",
    "delta_df = (\n",
    "    df.join(previous_df, on=[\"contestant\", \"time\"], how=\"left\")\n",
    "    .join(df, left_on=[\"contestant\", \"previous_time\"], right_on=[\"contestant\", \"time\"], how=\"left\", suffix=\"_prev\")\n",
    "    .with_column(\"delta_from_previous\", col(\"chocolates\") - col(\"chocolates_prev\").fill_null(0))\n",
    "    .select(\"contestant\", \"time\", \"delta_from_previous\")\n",
    ")\n",
    "\n",
    "joined_spree = df.join(window_df, on=[\"contestant\", \"time\"], how=\"left\")\n",
    "spree_rank_df = (\n",
    "    joined_spree.join(joined_spree, left_on=\"contestant\", right_on=\"contestant\", suffix=\"_r\", how=\"inner\")\n",
    "    .where(col(\"chocolates_within_100min_r\") > col(\"chocolates_within_100min\"))\n",
    "    .groupby([\"contestant\", \"time\"])\n",
    "    .agg((col(\"chocolates_within_100min_r\").count() + 1).alias(\"time_window_chocolates_rank\"))\n",
    ")\n",
    "\n",
    "final_df = (\n",
    "    df.join(cumulative_df, on=[\"contestant\", \"time\"], how=\"left\")\n",
    "    .join(window_df, on=[\"contestant\", \"time\"], how=\"left\")\n",
    "    .join(rank_df, on=[\"contestant\", \"time\", \"chocolates\"], how=\"left\")\n",
    "    .join(delta_df, on=[\"contestant\", \"time\"], how=\"left\")\n",
    "    .join(spree_rank_df, on=[\"contestant\", \"time\"], how=\"left\")\n",
    "    .with_column(\"cumulative_chocolates\", col(\"cumulative_chocolates\").cast(\"int\"))\n",
    "    .with_column(\"chocolates_within_100min\", col(\"chocolates_within_100min\").cast(\"int\"))\n",
    "    .with_column(\"session_rank\", col(\"session_rank\").fill_null(1).cast(\"int\"))\n",
    "    .with_column(\"delta_from_previous\", col(\"delta_from_previous\").fill_null(0).cast(\"int\"))\n",
    "    .with_column(\"time_window_chocolates_rank\", col(\"time_window_chocolates_rank\").fill_null(1).cast(\"int\"))\n",
    "    .select(\n",
    "        \"contestant\",\n",
    "        \"time\",\n",
    "        \"chocolates\",\n",
    "        \"cumulative_chocolates\",\n",
    "        \"chocolates_within_100min\",\n",
    "        \"session_rank\",\n",
    "        \"delta_from_previous\",\n",
    "        \"time_window_chocolates_rank\",\n",
    "    )\n",
    "    .sort([\"contestant\", \"time\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's over 50 lines of complex code with multiple joins, aggregations, and transformations -- compared to 8 lines with window functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion: The Power of Window Functions\n",
    "\n",
    "Through this fun example of a tortoise vs hare chocolate eating contest, we've demonstrated how window functions can dramatically simplify compelx analytical tasks:\n",
    "1. **Conciseness**: Window functions reduced our code from over 50 lines to just 8 lines\n",
    "2. **Readability**: The window function approach is far more intuitive and easier to understand\n",
    "3. **Performance**: Window functions are typically more efficient than equivalent operations with joins and subqueries\n",
    "4. **Flexibility**: We can easily combine different window specifications to answer a wide range of analytical questions.\n",
    "\n",
    "Key window function concepts we covered:\n",
    "* Basic partitioning with [`partition_by()`](https://docs.daft.ai/en/stable/api/window/#daft.window.Window.partition_by)\n",
    "* Ordering rows with partitions using [`order_by()`](https://docs.daft.ai/en/stable/api/window/#daft.window.Window.order_by)\n",
    "* Defining custom frames with [`rows_between()`](https://docs.daft.ai/en/stable/api/window/#daft.window.Window.rows_between) and [`range_between()`](https://docs.daft.ai/en/stable/api/window/#daft.window.Window.range_between)\n",
    "* Using aggregation functions ([`sum()`](https://docs.daft.ai/en/stable/api/aggregations/#daft.dataframe.dataframe.GroupedDataFrame.sum)) and ranking functions ([`rank()`](https://docs.daft.ai/en/stable/api/window/#daft.functions.rank) and [`dense_rank()`](https://docs.daft.ai/en/stable/api/window/#daft.functions.dense_rank))\n",
    "\n",
    "If you find yourself writing complex self-joins or struggling with cummulative calculations, try [Daft's window functions](https://docs.daft.ai/en/stable/api/window/#window-functions) instead!"
   ]
  }
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